def eval_one_epoch(self, epoch):
# Input the uncorrupted training data
pred_y = self.session.run(
self.pred_y,
feed_dict={
self.ratings:
self.train_array,
# self.output_mask: self.negative_output_mask,
self.uid:
range(self.num_user),
self.istraining:
False,
self.isnegsample:
self.is_neg_sa,
self.layer1_dropout_rate:
0
})
pred_y = pred_y.clip(min=0, max=1)
n_batches = 0
total_hr, total_ndcg = np.zeros(len(self.topK)), np.zeros(
len(self.topK))
# Loop for each user (generate the ranking lists for different users)
for u in self.ranking_dict:
iid = self.ranking_dict[u] # The ranking item ids for user u
rk = pred_y[u,
np.array(iid)] # The predicted item values for user u
n_batches += 1
hr, ndcg = evl.rankingMetrics(rk,
iid,
self.topK,
self.test_dict[u],
mod='hr')
total_hr += hr
total_ndcg += ndcg
avg_hr, avg_ndcg = total_hr / n_batches, total_ndcg / n_batches
for i in range(len(self.topK)):
print('-' * 55)
print("Epoch {0}: [HR@{1}] {2}".format(epoch, self.topK[i],
avg_hr[i]))
print("Epoch {0}: [nDCG@{1}] {2}".format(epoch, self.topK[i],
avg_ndcg[i]))
print('=' * 55)
return avg_hr[0], avg_ndcg[0]
def eval_one_epoch(self, epoch):
# Input the uncorrupted training data
pred_y = self.session.run(
self.pred_y,
feed_dict={
self.ratings:
self.train_array,
# self.output_mask: self.negative_output_mask,
self.uid:
range(self.num_user),
self.istraining:
False,
self.isnegsample:
self.is_neg_sa,
self.layer1_dropout_rate:
0
})
pred_y = pred_y.clip(min=0, max=1)
n_batches, total_prec, total_recall, total_ap = 0, 0, 0, 0
# Loop for each user (generate the ranking lists for different users)
for u in self.ranking_dict:
iid = self.ranking_dict[u] # The ranking item ids for user u
rk = pred_y[u,
np.array(iid)] # The predicted item values for user u
prec, recall, ap = evl.rankingMetrics(rk,
iid,
self.topK,
self.test_dict[u],
mod='precision')
n_batches += 1
total_prec += prec
total_recall += recall
total_ap += ap
avg_prec, avg_recall, avg_ap = total_prec / n_batches, total_recall / n_batches, total_ap / n_batches
print("Epoch {0}: [Precision@{1}] {2}".format(epoch, self.topK,
avg_prec))
print("Epoch {0}: [Recall@{1}] {2}".format(epoch, self.topK,
avg_recall))
print("Epoch {0}: [MAP@{1}] {2}".format(epoch, self.topK, avg_ap))
print("=" * 40)
return avg_prec, avg_recall, avg_ap
def eval_one_epoch(self, epoch):
n_batches, total_hr, total_ndcg, total_mrr = 0, 0, 0, 0
for u in self.ranking_dict:
iid = self.ranking_dict[u]
uid = [u] * len(iid)
rk = self.session.run(self.pred_y,
feed_dict={
self.uid: uid,
self.iid: iid
})
hr, ndcg, mrr = evl.rankingMetrics(rk, iid, self.topk,
self.test_dict[u])
n_batches += 1
total_hr += hr
total_ndcg += ndcg
total_mrr += mrr
print("Epoch {0}: [HR] {1} and [MRR] {2} and [nDCG@{3}] {4}".format(
epoch, total_hr / n_batches, total_mrr / n_batches, self.topk,
total_ndcg / n_batches))
def eval_one_epoch(self, epoch):
n_batches, total_prec, total_recall, total_ap = 0, 0, 0, 0
# n_batches, total_hr, total_ndcg, total_mrr = 0, 0, 0, 0
for u in self.ranking_dict:
if len(self.test_dict[u]) == 0:
continue
iid = self.ranking_dict[u]
uid = [u] * len(iid)
rk = self.session.run(self.pred_y,
feed_dict={
self.uid: uid,
self.iid: iid
})
# hr, ndcg, mrr = evl.rankingMetrics(rk, iid, self.topK, self.test_dict[u], mod='hr')
prec, recall, _, _, _ = evl.rankingMetrics(rk,
iid, [self.topK],
self.test_dict[u],
mod='precision',
is_map=False)
n_batches += 1
# total_hr += hr
# total_ndcg += ndcg
# total_mrr += mrr
total_prec += prec[0]
total_recall += recall[0]
# avg_hr, avg_mrr, avg_ndcg = total_hr / n_batches, total_mrr / n_batches, total_ndcg / n_batches
# print("Epoch {0}: [HR] {1} and [MRR] {2} and [nDCG@{3}] {4}".format(epoch, avg_hr, avg_mrr, self.topK, avg_ndcg))
# return avg_hr, avg_mrr,avg_ndcg
avg_prec, avg_recall, avg_ap = total_prec / n_batches, total_recall / n_batches, total_ap / n_batches
print("Epoch {0}: [Precision@{1}] {2}".format(epoch, self.topK,
avg_prec))
print("Epoch {0}: [Recall@{1}] {2}".format(epoch, self.topK,
avg_recall))
print("Epoch {0}: [MAP@{1}] {2}".format(epoch, self.topK, avg_ap))
print("=" * 40)
return avg_prec, avg_recall, avg_ap
def eval_one_epoch(self, epoch):
n_batches = 0
total_hr, total_ndcg = np.zeros(len(self.topk)), np.zeros(len(self.topk))
for u in self.ranking_dict:
iid = self.ranking_dict[u]
uid = [u] * len(iid)
rk = self.session.run(self.pred_y, feed_dict={self.uid: uid, self.iid: iid})
n_batches += 1
hr, ndcg = evl.rankingMetrics(rk, iid, self.topk, self.test_dict[u], mod='hr')
total_hr += hr
total_ndcg += ndcg
avg_hr, avg_ndcg = total_hr / n_batches, total_ndcg / n_batches
for i in range(len(self.topk)):
print('-' * 55)
print("Epoch {0}: [HR@{1}] {2}".format(epoch, self.topk[i], avg_hr[i]))
print("Epoch {0}: [nDCG@{1}] {2}".format(epoch, self.topk[i], avg_ndcg[i]))
print('=' * 55)
return avg_hr[0], avg_ndcg[0]
def eval_one_epoch(self, epoch):
delta = self.session.run(self.update_delta,
feed_dict={self.ratings: self.train_array, self.output_mask: self.negative_output_mask,
self.istraining: False, self.isnegsample: False,
self.layer1_dropout_rate: 0})
layer2_w, layer2_w_org = self.session.run([self.w2, self.w2_org],
feed_dict={self.ratings: self.train_array,
self.output_mask: self.negative_output_mask,
self.istraining: False, self.isnegsample: False,
self.layer1_dropout_rate: 0})
print("Evaluation Epoch {0}: [Delta] = {1} [W2]={2} [W2_ORG]={3}".format(epoch, delta[10,0], layer2_w[10,0], layer2_w_org[10,0]))
# Input the uncorrupted training data
pred_y = self.session.run(self.pred_y,
feed_dict={self.ratings: self.train_array, self.output_mask: self.negative_output_mask,
self.istraining:False, self.isnegsample:False,
self.layer1_dropout_rate: 0})
pred_y = pred_y.clip(min=0,max=1)
# print("Prediction:{0}".format(pred_y[0]))
n_batches, total_hr, total_ndcg, total_mrr = 0, 0, 0, 0
# Loop for each user (generate the ranking lists for different users)
for u in self.ranking_dict:
iid = self.ranking_dict[u] # The ranking item ids for user u
rk = pred_y[u, :][np.array(iid)] # The predicted item values for user u
hr, ndcg, mrr = evl.rankingMetrics(rk, iid, self.topK, self.test_dict[u])
n_batches += 1
total_hr += hr
total_ndcg += ndcg
total_mrr += mrr
# if u == 0:
# print(len(pred_y[u,:]), len(rk), len(iid),self.test_dict[u], hr)
avg_hr, avg_mrr, avg_ndcg = total_hr / n_batches, total_mrr / n_batches, total_ndcg / n_batches
print("Epoch {0}: [HR] {1} and [MRR] {2} and [nDCG@{3}] {4}".format(epoch, avg_hr, avg_mrr, self.topK, avg_ndcg))
return avg_hr, avg_mrr, avg_ndcg
def eval_one_epoch(self, epoch):
# Input the uncorrupted training data
pred_y = self.session.run(self.pred_y,
feed_dict={self.ratings: self.train_array, self.output_mask: self.negative_output_mask,
self.istraining:False, self.isnegsample:False,
self.layer1_dropout_rate: 0})
pred_y = pred_y.clip(min=0,max=1)
n_batches, total_hr, total_ndcg, total_mrr = 0, 0, 0, 0
# Loop for each user (generate the ranking lists for different users)
for u in self.ranking_dict:
iid = self.ranking_dict[u] # The ranking item ids for user u
rk = pred_y[u, :][np.array(iid)] # The predicted item values for user u
hr, ndcg, mrr = evl.rankingMetrics(rk, iid, self.topK, self.test_dict[u])
n_batches += 1
total_hr += hr
total_ndcg += ndcg
total_mrr += mrr
avg_hr, avg_mrr, avg_ndcg = total_hr / n_batches, total_mrr / n_batches, total_ndcg / n_batches
print("Epoch {0}: [HR] {1} and [MRR] {2} and [nDCG@{3}] {4}".format(epoch, avg_hr, avg_mrr, self.topK, avg_ndcg))
return avg_hr, avg_mrr, avg_ndcg
def eval_one_epoch(self, epoch):
n_batches = 0
if self.is_prec:
total_prec, total_recall = np.zeros(len(self.topK)), np.zeros(
len(self.topK))
else:
total_hr, total_ndcg = np.zeros(len(self.topK)), np.zeros(
len(self.topK))
# if self.robust_test:
# print('[Eps={0}] {1} Noise Level [Robust Test]'.format(self.eps, self.noise_type))
# self.session.run([self.update_P, self.update_Q], feed_dict={self.uid: uid, self.pos_iid: iid})
for u in self.ranking_dict:
if len(self.test_dict[u]) == 0:
continue
iid = self.ranking_dict[u]
uid = [u] * len(iid)
n_batches += 1
if self.is_adv:
rk = self.session.run(self.pred_y_pos_adv,
feed_dict={
self.uid: uid,
self.pos_iid: iid
})
else:
rk = self.session.run(self.pred_y_pos,
feed_dict={
self.uid: uid,
self.pos_iid: iid
})
if self.is_prec:
precision, recall, _, _, _ = evl.rankingMetrics(
rk,
iid,
self.topK,
self.test_dict[u],
mod='precision',
is_map=False)
total_prec += precision
total_recall += recall
else:
hr, ndcg = evl.rankingMetrics(rk,
iid,
self.topK,
self.test_dict[u],
mod='hr')
total_hr += hr
total_ndcg += ndcg
if self.is_prec:
avg_prec, avg_recall = total_prec / n_batches, total_recall / n_batches
for i in range(len(self.topK)):
print('-' * 55)
print("Epoch {0}: [Precision@{1}] {2}".format(
epoch, self.topK[i], avg_prec[i]))
print("Epoch {0}: [Recall@{1}] {2}".format(
epoch, self.topK[i], avg_recall[i]))
print('=' * 55)
return avg_prec[0], avg_recall[0]
else:
avg_hr, avg_ndcg = total_hr / n_batches, total_ndcg / n_batches
for i in range(len(self.topK)):
print('-' * 55)
print("Epoch {0}: [HR@{1}] {2}".format(epoch, self.topK[i],
avg_hr[i]))
print("Epoch {0}: [nDCG@{1}] {2}".format(
epoch, self.topK[i], avg_ndcg[i]))
print('=' * 55)
return avg_hr[0], avg_ndcg[0]
_, ranking_dict, test_dict = mtl.negdict_mat(original_matrix,
test_matrix,
num_neg=199,
mod='others',
random_state=10)
for user in ranking_dict:
if len(test_dict[user]) == 0:
continue
iid = ranking_dict[user] # The ranking item ids for user u
rk = item_pop_arr[np.asarray(iid)]
print(rk)
hr, ndcg = evl.rankingMetrics(rk, iid, topK, test_dict[user], mod='hr')
total_hr += hr
total_ndcg += ndcg
avg_hr, avg_ndcg = total_hr / num_user, total_ndcg / num_user
for i in range(len(topK)):
print('-' * 55)
print("[HR@{0}] {1}".format(topK[i], avg_hr[i]))
print("[nDCG@{0}] {1}".format(topK[i], avg_ndcg[i]))
print('=' * 55)
save_path = "Result/%s/ItemPop/%s/" % (dataset, date)
if not os.path.exists(save_path):
os.makedirs(save_path)
def eval_one_epoch(self, epoch):
if self.is_user_node:
feed_dict = {
self.ratings: self.train_array,
self.uid: range(self.num_user),
self.istraining: False,
self.layer1_dropout_rate: 0
}
else:
feed_dict = {
self.ratings: self.train_array,
self.istraining: False,
self.layer1_dropout_rate: 0
}
if self.robust_test:
print('[Pos={0}] {1} Noise Added [Robust Test]'.format(
self.noise_pos, self.noise_type))
print('[Eps={0}] {1} Noise Level [Robust Test]'.format(
self.eps, self.noise_type))
self.session.run(self.update_delta, feed_dict)
# Input the uncorrupted training data
pred_y = self.session.run(self.pred_y, feed_dict)
# pred_y = pred_y.clip(min=0,max=1)
n_batches = 0
if self.is_prec:
total_prec, total_recall = np.zeros(len(self.topK)), np.zeros(
len(self.topK))
else:
total_hr, total_ndcg = np.zeros(len(self.topK)), np.zeros(
len(self.topK))
# Loop for each user (generate the ranking lists for different users)
for u in self.ranking_dict:
if len(self.test_dict[u]) == 0:
continue
iid = self.ranking_dict[u] # The ranking item ids for user u
rk = pred_y[u,
np.array(iid)] # The predicted item values for user u
n_batches += 1
if self.is_prec:
precision, recall, _, _, _ = evl.rankingMetrics(
rk,
iid,
self.topK,
self.test_dict[u],
mod='precision',
is_map=False)
total_prec += precision
total_recall += recall
else:
hr, ndcg = evl.rankingMetrics(rk,
iid,
self.topK,
self.test_dict[u],
mod='hr')
total_hr += hr
total_ndcg += ndcg
if self.is_prec:
avg_prec, avg_recall = total_prec / n_batches, total_recall / n_batches
for i in range(len(self.topK)):
print('-' * 55)
print("Epoch {0}: [Precision@{1}] {2}".format(
epoch, self.topK[i], avg_prec[i]))
print("Epoch {0}: [Recall@{1}] {2}".format(
epoch, self.topK[i], avg_recall[i]))
print('=' * 55)
return avg_prec[0], avg_recall[0]
else:
avg_hr, avg_ndcg = total_hr / n_batches, total_ndcg / n_batches
for i in range(len(self.topK)):
print('-' * 55)
print("Epoch {0}: [HR@{1}] {2}".format(epoch, self.topK[i],
avg_hr[i]))
print("Epoch {0}: [nDCG@{1}] {2}".format(
epoch, self.topK[i], avg_ndcg[i]))
print('=' * 55)
return avg_hr[0], avg_ndcg[0]
def eval_one_epoch(self, epoch):
if self.robust_test:
delta = self.session.run(self.update_delta,
feed_dict={
self.ratings: self.train_array,
self.output_mask:
self.negative_output_mask,
self.istraining: False,
self.isnegsample: False,
self.add_hidden_noise:
self.hidden_noise,
self.add_weight_noise:
self.weight_noise,
self.layer1_dropout_rate: 0
})
layer2_w, layer2_w_org = self.session.run(
[self.w2, self.w2_org],
feed_dict={
self.ratings: self.train_array,
self.output_mask: self.negative_output_mask,
self.istraining: False,
self.isnegsample: False,
self.add_hidden_noise: self.hidden_noise,
self.add_weight_noise: self.weight_noise,
self.layer1_dropout_rate: 0
})
print("Evaluation Epoch {0}: [Delta] = {1} [W2]={2} [W2_ORG]={3}".
format(epoch, delta[10, 0], layer2_w[10, 0],
layer2_w_org[10, 0]))
# Input the uncorrupted training data
pred_y = self.session.run(self.pred_y,
feed_dict={
self.ratings: self.train_array,
self.output_mask:
self.negative_output_mask,
self.istraining: False,
self.isnegsample: self.is_neg_sa,
self.add_hidden_noise: self.hidden_noise,
self.add_weight_noise: self.weight_noise,
self.layer1_dropout_rate: 0
})
pred_y = pred_y.clip(min=0, max=1)
# n_batches, total_prec, total_ap = 0, 0, 0
n_batches, total_hr, total_ndcg = 0, 0, 0
# Loop for each user (generate the ranking lists for different users)
for u in self.ranking_dict:
iid = self.ranking_dict[u] # The ranking item ids for user u
rk = pred_y[u,
np.array(iid)] # The predicted item values for user u
hr, ndcg = evl.rankingMetrics(rk,
iid,
self.topK,
self.test_dict[u],
mod='hr')
n_batches += 1
total_hr += hr
total_ndcg += ndcg
# total_prec += precision
# total_ap += ap
avg_hr, avg_ndcg = total_hr / n_batches, total_ndcg / n_batches
print('=' * 50)
print("Epoch {0}: [HR@{1}] {2}".format(epoch, self.topK, avg_hr))
print("Epoch {0}: [nDCG@{1}] {2}".format(epoch, self.topK, avg_ndcg))
return avg_hr, avg_ndcg